The GATE Data Science and AI (DA) exam has gained popularity among those students and young professionals who are interested in data-driven careers. As the syllabus is broad and covers many domains such as math, programming, databases, machine learning, and artificial intelligence, it is very important to know what exactly should be studied.
Before making any study plan or attempting any practice questions, it is important to have an overview of the complete GATE Data Science and AI syllabus and its section-wise weightage. Here we will provide the syllabus for GATE DA 2026 in detail, along with the weightage and other preparation tips.
GATE DA 2026: Paper Pattern, Eligibility & Overview
Before proceeding to prepare for the GATE examination, it is necessary to get familiar with the GATE DA examination pattern, structure, and eligibility criteria. The Data Science and Artificial Intelligence (DA) examination will test the candidate on mathematics, programming, databases, machine learning, and AI through objective questions and numerical answer-type questions.
GATE DA 2026 Paper Pattern
| Particulars | Details |
| Exam Mode | Computer-Based Test (CBT) |
| Duration | 3 Hours |
| Total Marks | 100 |
| Total Questions | 65 |
| Question Types | MCQ, MSQ, and NAT |
| General Aptitude Weightage | 15 Marks |
| Data Science & AI Weightage | 85 Marks |
| Language | English |
| Negative Marking | Applicable only for MCQs |
Eligibility for GATE DA 2026
Eligibility for GATE is open to all candidates pursuing or completing their graduation in engineering, technology, science, or other relevant fields. But eligibility norms may be different depending on the current norms for GATE exams.
As eligibility norms and exam norms may differ from one year to another year, it is advised that the candidate should refer to the latest norms of GATE 2026 notification prior to the application process.
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GATE Data Science & AI Syllabus 2026 at a Glance (Section Weightage)
GATE Data Science syllabus weightage will give you an idea about important topics and help you in making a more effective study plan. Although the weightage will differ every year, the following table will give you an approximate weightage according to the recently held GATE DA exams.
Read More: Data Science Course Syllabus and Subjects
The table below provides a section-wise overview of the GATE DA syllabus and its approximate weightage.
| Section | Major Topics | Indicative Weightage (Marks) |
| General Aptitude | Verbal Ability, Numerical Ability, Logical Reasoning | 15 |
| Probability & Statistics | Probability, Random Variables, Distributions, Hypothesis Testing | 16 |
| Linear Algebra | Matrices, Eigenvalues, Eigenvectors, Vector Spaces | 10 |
| Calculus & Optimization | Differentiation, Integration, Partial Derivatives, Optimization | 8 |
| Programming, Data Structures & Algorithms | Python, Data Structures, Searching, Sorting, Graph Algorithms | 21 |
| Database Management & Warehousing | ER Model, Relational Algebra, SQL, Normalization, Data Warehousing | 8 |
| Machine Learning | Supervised Learning, Unsupervised Learning, Model Evaluation | 11 |
| Artificial Intelligence | Search Algorithms, Logic, Reasoning, Knowledge Representation | 11 |
Based on previous-year analysis, Programming, Data Structures & Algorithms, Probability & Statistics, Machine Learning, and Artificial Intelligence are among the most important areas for scoring well in GATE DA. However, a balanced preparation strategy covering the complete syllabus is recommended for a competitive rank.
You can also check out: Artificial Intelligence Courses Syllabus
Section 1: Probability & Statistics
Probability and Statistics serve as the mathematical backbone for various aspects of data science, machine learning, and AI. Probability and Statistics constitute a very important topic in the GATE DA examination, wherein you will be assessed not only on theoretical knowledge but also on the ability to solve numerical questions.
The GATE Probability and Statistics syllabus includes:
- Probability and Conditional Probability
- Bayes’ Theorem and Independence of Events
- Random Variables
- Bernoulli, Binomial, Poisson, Uniform, Exponential, and Normal Distributions
- Mean, Median, Mode, Variance, and Standard Deviation
- Statistical Estimation and Confidence Intervals
- Hypothesis Testing
- Correlation and Regression
A strong understanding of probability distributions, Bayes’ theorem, and hypothesis testing can be particularly useful, as these concepts frequently appear in machine learning and data analysis problems.
You can check this out as well: Probability in Excel: What should you use?
Section 2: Linear Algebra & Calculus
Linear algebra and calculus have a significant place in machine learning, optimization, and analysis. Various machine learning techniques make use of matrices, vectors, derivatives, and optimization concepts, and hence, Linear Algebra and Calculus form an important component of the GATE DA syllabus.
The GATE Linear Algebra and Calculus syllabus includes:
- Matrices and Matrix Operations
- Systems of Linear Equations
- Vector Spaces, Basis, and Dimension
- Eigenvalues and Eigenvectors
- Differentiation and Partial Derivatives
- Integration
- Vector Calculus
- Maxima and Minima
- Optimization Basics
An in-depth knowledge of matrices, eigenvalues, eigenvectors, and optimization would be especially useful since all of these subjects are often found within algorithms used in machine learning and data science.
Learn more: Linear Algebra for Data Science
Section 3: Programming, Data Structures & Algorithms
The GATE DA Programming section assesses your efficiency in writing programs and solving computational problems by using basic data structures and algorithms. As the topic of programming is very skill-oriented, regular practice becomes more important than theoretical learning.
The syllabus includes:
- Python Programming
- Arrays, Linked Lists, Stacks, Queues, Trees, and Graphs
- Recursion
- Searching and Sorting Algorithms
- Hashing
- Algorithm Analysis and Time Complexity
- Basic Graph Algorithms
To do well in this part, the student should concentrate on mastering the basic data structures, algorithm analysis, and problem-solving using code questions. Solving questions involving implementations will help to develop speed and accuracy in the test.
To start with your learning journey, you can begin with: Free Data Science Course with Certificate
Section 4: Database Management & Warehousing
The understanding of database concepts is very crucial for the storage and retrieval of large amounts of data. The GATE DBMS syllabus includes relational database concepts, SQL, normalization, and data warehousing, which are popularly used in data engineering and analysis.
The syllabus includes:
- Entity-Relationship (ER) Model
- Relational Model
- Relational Algebra
- SQL Queries
- Functional Dependencies
- Normalization
- Transactions and Concurrency Control
- Data Warehousing Concepts
As SQL and normalization are the basis of designing databases, the candidate must concentrate on gaining knowledge about query writing, relational algebra, and normalization. This subject is commonly covered in examinations and has real-life applications in data science.
Learn more about database concepts for free with these Programming and Coding Courses Online
Section 5: Machine Learning
Machine Learning is one of the most important sections of the GATE DA paper and often carries significant weightage. The GATE Machine Learning syllabus includes the core concepts used to build predictive models, identify patterns in data, and evaluate model performance.
The syllabus includes:
- Supervised and Unsupervised Learning
- Linear Regression
- Classification Algorithms
- Clustering Techniques
- Feature Selection and Dimensionality Reduction
- Bias-Variance Tradeoff
- Overfitting and Underfitting
- Model Evaluation Metrics
- Cross-Validation
Candidates should pay special attention to supervised learning, regression, classification, clustering, and model evaluation techniques. These topics frequently appear in GATE DA questions and also form the foundation for advanced AI and data science concepts.
Section 6: Artificial Intelligence
The GATE AI syllabus contains the basic principles of problem-solving and decision-making capabilities of machines. This topic emphasizes the basics of searching, logic, and knowledge representation, which are some of the main components of artificial intelligence.
The syllabus includes:
- State Space Search
- Uninformed Search Algorithms
- Informed Search Algorithms
- Heuristic Search
- Knowledge Representation
- Propositional Logic
- First-Order Logic
- Reasoning and Inference
- Constraint Satisfaction Problems
You can also go through the fundamentals of deep learning since it is useful for understanding modern AI applications, but it is not a major standalone topic in the current GATE DA syllabus.
The candidates must give their attention to search algorithms, logical reasoning, and other related topics because these subjects often come up in the examination and are important in the implementation of artificial intelligence programs. Knowledge of basic deep learning can help in understanding modern artificial intelligence applications.
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How to Prepare for GATE DA 2026 (Study Plan & Resources)
An effective GATE Data Science and AI preparation requires a balanced approach of building concepts, solving problems, revising, and giving mock tests. As the course structure includes Mathematics, Programming, Databases, Machine Learning, and Artificial Intelligence, a well-structured study plan will help you to get coverage of all these topics.
Suggested 6-Month Study Plan
| Timeline | Focus Areas |
| Months 1 – 2 | Build fundamentals in Probability, Statistics, Linear Algebra, and Calculus. Focus on understanding concepts and solving basic problems. |
| Months 2 – 3 | Cover Programming, Data Structures, Algorithms, and DBMS. Practice coding problems and SQL queries regularly. |
| Months 3 – 4 | Study Machine Learning and Artificial Intelligence topics. Focus on core algorithms, model evaluation, search techniques, and reasoning concepts. |
| Month 5 | Revise all sections and solve previous-year GATE questions. Identify weak areas and revisit important concepts. |
| Month 6 | Attempt full-length mock tests, improve time management, and focus on revision rather than learning new topics. |
Recommended Resources
- Official GATE DA syllabus and notifications
- NPTEL courses for Mathematics, Machine Learning, and Artificial Intelligence
- Previous-year GATE question papers
- Standard textbooks for Probability, Linear Algebra, Algorithms, DBMS, and Machine Learning include works by Sheldon Ross, Gilbert Strang, Cormen et al. (CLRS), Silberschatz, Korth & Sudarshan, Christopher Bishop, and Tom Mitchell.
- High-quality mock tests and practice sets
Mock Test Strategy
Practice with mock tests should definitely be an integral part of your preparation in the last two months. Besides boosting up your marks, you should concentrate on the analysis of your mistakes and work on your weaknesses, especially time management. It would be much more useful to practice regularly than to learn something new before the exam.
Careers After GATE DA: M.Tech, PSUs & Industry Roles
A good GATE DA score can open up opportunities in higher education, research, public sector recruitment, and industry. Depending on their interests and career goals, candidates can use their GATE score for admissions, research opportunities, or professional roles in data science and artificial intelligence.
Some common GATE DA career options include:
- M.Tech and postgraduate programs in Data Science, Artificial Intelligence, Machine Learning, and related fields
- Research opportunities in universities and research institutions
- PSU recruitment through GATE, where applicable
- Job profiles like Data Scientist, Data Analyst, Machine Learning Engineer, AI Engineer, Business Intelligence Analyst, etc.
Career options usually depend on whether the candidate is interested in higher studies/research or joining the industry right after completing their education.
Explore these career paths in more detail: Data Science Career Guide for 2025-2026
FAQs (for FAQ Schema)
Q1. What is the syllabus for GATE Data Science and AI (DA) 2026?
The syllabus contains topics like Probability & Statistics, Linear Algebra & Calculus, Programming, Data Structures & Algorithms, DBMS, Machine Learning, Artificial Intelligence, and General Aptitude.
Q2. How much weightage does machine learning carry in GATE DA?
Among all these topics, Machine Learning has been one of the more important sections in recent GATE DA papers, although the exact weightage changes every year.
Q3. Is GATE Data Science and AI tough to crack?
The level of toughness varies according to your preparation and background. The students who have good knowledge of Mathematics, Programming, and Machine Learning find it easier.
Q4. What is the eligibility for GATE DA 2026?
Candidates pursuing or completing an undergraduate degree in engineering, technology, science, or other eligible disciplines can apply, subject to the latest GATE eligibility criteria.
Q5. Which resources are best for GATE DA preparation?
Some of the best sources are NPTEL videos, books, previous year’s question papers, mock tests, and other training programs.
Q6. What careers open up after clearing GATE DA?
A high score in GATE DA will assist you in securing admission into an M.Tech program, doing research, getting jobs in PSUs, and data science, machine learning, and AI in industry.
